1. Field of the Invention
The present invention relates to data processing, and more particularly, to a method and apparatus of selecting N metrics among M metrics.
2. Related Art
A wireless communication system has been prevalently developed in order to provide various kinds of communication services such as audio, data, or the like. Generally, the wireless communication system is a multiple access system that may share available system resources (bandwidth, transmission power, or the like) to support communication with multi-user. Examples of a multiple access system may include a code division multiple access (CDMA) system, a frequency division multiple access (FDMA) system, a time division multiple access (TDMA) system, an orthogonal frequency division multiple access (OFDMA) system, a single carrier frequency division multiple access (SC-FDMA) system, or the like.
Multiple input multiple output (MIMO) is a technology that has been interested due to characteristics that a throughput increases without allocating additional resources. Channel capacity of the MIMO system mainly depends on a detection method used in a receiver in order to restore a received signal. Therefore, a detection method implementing both of high performance and low complexity has been interested.
Maximum likelihood (ML) detection has been known as a MIMO detection method that may obtain most ideal performance by searching all signal vectors in order to restore a modulated signal. However, the ML detection has a problem that complexity exponentially increases as the number of transmit antennas increases.
QR-decomposition with M-algorithm (QRD-M) is one of methods developed as an alternative to the ML detection having high complexity. A mobile terminal uses limited power and is vulnerable to latency. Therefore, it is difficult to use a detection method having a high deviation and arbitrarily changed complexity. The QRD-M trades off between bit error rate (BER) performance and complexity to have fixed complexity and excellent BER performance.
The QRD-M has a tree-search structure having a trade-off between performance and a calculation amount according to a size of M, which is the number of survivor paths. However, since a large M value including accurate paths in all steps should be used in order to approach performance of the ML detection, a large calculation amount is required.